
#chapter 3

import tensorflow as tf
sess = tf.InteractiveSession()

#step 1: define network forward function

x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b=tf.Variable(tf.zeros([10]))
y=tf.nn.softmax(tf.matmul(x, W) +b)

#end step 1


#step 2a define loss function

y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy=tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y), reduction_indices=[1]))

#end step 2a


#step 2b define optimizer

train_step=tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

#end step 2b

#step 2c define evaluate function

correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

#end step 2c

tf.global_variables_initializer().run()

#step 3a load training set

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

#end step 3a

#step 3b loop training

for i in range(1000):
	batch_xs, batch_ys = mnist.train.next_batch(100)
	
	if i%100==0:
		train_accuracy=accuracy.eval(feed_dict={x:batch_xs, y_:batch_ys})
		print("step %d, training accuracy %g"%(i, train_accuracy))	
	
	train_step.run({x: batch_xs, y_: batch_ys})
	
#end step 3b


#step 4 evaluate on test set

print(accuracy.eval({x: mnist.test.images, y_:mnist.test.labels}))

#end step 4

import numpy
numpy.savetxt("/home/u/b.csv", b.eval(), delimiter=",")
numpy.savetxt("/home/u/W.csv", W.eval(), delimiter=",")

